There is a wide array of technologies directed to in vivo imaging of mammals—for example, bioluminescence, fluorescence, tomography, and multimodal imaging technologies. In vivo imaging of small mammals is performed by a large community of investigators in various fields, e.g., oncology, infectious disease, and drug discovery.
In vivo micro computed tomography (hereafter, “microCT”) imaging, is an x-ray-based technology that can image tissues, organs, and non-organic structures with high resolution, although higher-throughput imaging may make beneficial use of lower resolution microCT imaging to speed image acquisition and/or processing while maintaining acceptable accuracy and image detail. MicroCT has evolved quickly, requiring low dose scanning and fast imaging protocols to facilitate multi-modal applications and enable longitudinal experimental models. In vivo imaging often involves the use of reagents, such as fluorescent probes, for non-invasive spatiotemporal visualization of biological phenomena inside a live animal. Multi-modal imaging involves the fusion of images obtained in different ways, for example, by combining FMT, PET, MRI, CT, and/or SPECT imaging data.
Image analysis applications and/or imaging systems generally allow for visualization, analysis, processing, segmentation, registration and measurement of biomedical images. These applications and systems also provide volume rendering tools (e.g., volumetric compositing, depth shading, gradient shading, maximum intensity projection, summed voxel projection, signal projection); manipulation functions (e.g., to define areas of structures of interest, delete unwanted objects, edit images and object maps); and measurement functions (e.g., for calculation of number of surface voxels, number of exposed faces, planar area of a region, and estimated surface area or volume of a region).
Conventional object segmentation algorithms typically rely on morphological approaches to perform bone splitting. In these approaches, watershed transformation is typically applied to the gray-scale image data or the distance transform of the binary object mask. The watershed transform separates the binary mask into labeled regions which represent the individual objects that make up the binary mask (as discussed, for example, in F. Meyer, S. Beucher, J. Vis. Comm. Im. Rep. 1(1), 1990 21-46). Using this approach, the binary bone mask from FIG. 1 can be separated and labeled as depicted in FIG. 2.
As evident in FIG. 2, this conventional segmentation approach suffers from limitations and drawbacks which result in under-segmentation, over-segmentation, and most importantly, incorrect placement of the split lines/planes. The latter occurs mainly because the combination of distance and watershed transforms place the split lines/planes on the thinnest connectors between the objects. However, the thinnest connectors may or may not coincide with the bone joints. This issue can be seen in the splitting of the femur from the pelvis in the results shown in FIG. 2.
While some existing morphological segmentation techniques may sometimes provide adequate accuracy in high-resolution images of larger mammals (e.g., humans), where the spacing between the objects is larger than the voxel resolution, their performance does not provide adequate accuracy needed for further image processing when dealing with low-resolution data such as the dataset shown in FIG. 1, e.g., for imaging of small animals (e.g., mice, rats, voles, rabbits, and similarly-sized animals). Micro-CT images typically have voxel sizes of about a few microns to a few hundred microns (e.g., between 4.5 microns to 200 microns). In lower resolutions (e.g., corresponding to voxel sizes of 40 microns or higher), partial volume effects can cause two separate objects to get morphologically connected in the binary mask, which can, for example cause significant loss in segmentation accuracy, as shown in FIG. 2. In some embodiments, the image of the subject (e.g., a small mammal subject, e.g., a mouse) has a resolution such that each voxel of the image corresponds to a volume at least 40 microns in each dimension.
While a variety of techniques have been used for splitting and segmentation of large bones in medical (human) CT imaging (e.g., as described by U.S. Pat. No. 8,306,305 B2 by Porat, et al.), there remains a need for robust methods for bone splitting and segmentation for small animal micro-CT imaging. For example, Porat et al. note in U.S. Pat. No. 8,306,305 B2 that the morphological operations described therein may not be appropriate if the method is intended to be used for the skull, since the operations would cause the entire brain to be included in the bone component. Porat et al. further describe that thin parts of the vertebrae may be less than one voxel thick, and the average density of the voxels that contain them may be less than their actual density and that these bones may not be found in their entirety.
A significant difference between the bone splitting and segmentation in large and small animals is that the spatial resolution offered by micro-CT is not always sufficiently higher than the thickness or dimensions of the bones in small animals, such as laboratory mice. This can pose further challenges on bone segmentation considering partial volume effects. Thus, there is a need for improved and accurate methods for automated object segmentation especially in cases such as small animal micro-CT data where high resolution images are not always available, or are too time-consuming or computationally complex to obtain.